People Are More Likely to Use Classification Rules When Features Are Easy to Describe Verbally

Bailey Brashears, a PhD in the Minda Cognitive Science lab, is presenting some research from her Master’s thesis as a poster at the 2019 Psychonomics Meeting in Montreal. The poster presentation is scheduled for the Friday Nov 15 poster session at noon. You can get a copy of the poster here

The Experiment

Bailey investigated the effects of feature verbalizablity on the acquisition of novel concepts. Participants in the experiment learned a category set that could be acquired by either a perfectly predictive criterial attribute rule or an overall family-resemblance strategy. Half of the participants learned this set with features that were easy to name and describe verbally and the other half learned this set with features that were not easy to name and describe verbally. In this case, easy to name meant features that corresponded to focal colours, nameable shapes, or countable shapes. Features that were not easy to name were equally diagnostic of category membership, but did not correspond to focal colours or nameable shapes.  

Some examples of the stimuli we used. Each pair shown are opposite prototypes

Results

After learning, participants were transferred to a set of new exemplars that included stimuli designed to distinguish between rule strategies and family-resemblance strategies. We found that it took participants about the same number of trials to learn the category sets regardless of the feature type learned. However, participants’ accuracy on previous items in the testing phase was higher for participants who learned the stimulus with easy to verbalize features; while participants were able to learn the categories in either condition, they retained these categories with better accuracy when the features were easy to verbalize. 

The majority of the subjects in the easily verbalizable condition were fit best by the criterial attribute (CA) rule model.

We also analyzed each learner’s performance with a set of computational models. Each model in the set assumed that performance was driven by one (and only one) of the available strategies (a rule, family resemblance, guessing, etc) and we examined which of the model best fit the observed performances. We found that people who learned the categories with easier to name features were more likely to classify new stimuli in accordance with a rule-based strategy. People who learned the categories with difficult to name features showed evidence of both rule use and family resemblance responding and no clear preference for either strategy. The figure above shows the models’ fit (essentially how close the model is to the observed data) for each subject by condition.

Implications

What do these results mean? For one thing, these results suggest that when people can rely on language to label features and to name things, they do. The more available the names are, the more likely people are to use rules that correspond to those features. This points to the primacy of language as a means to consolidate information. This language-based rule system might be a default way to acquire new concepts. It also means that there are other ways to learn concepts beyond using language, however. People who learned the categories that had features that were hard to describe verbally often learned the overall family resemblance, suggesting a tendency to learn exemplars. 

Bailey is working on a second study to replicate these results and plans to develop other ways to explore the role of language in acquiring new concepts. As well, she’s working on designing gaming environments that can explore the incidental acquisition of concepts. Stay tuned for more!


This work was supported by NSERC, Western University, and BrainsCAN.

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